{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'A':range(10),'B':range(5,15)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d1ae69ce80>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d1ae6aa860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.line(title='Info')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1d1aff37dd8>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d1aff1ac18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df.A, label='A')\n",
    "plt.plot(df.B, label='B')\n",
    "plt.xlabel('index')\n",
    "plt.ylabel('values')\n",
    "plt.title('Info')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1d1af80fd30>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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C\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d1afbe5438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x=df.A,y=df.B, s=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "df = pd.DataFrame({'X1': np.arange(10)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['X2'] = df.X1 * df.X1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['X3'] = df.X1 * df.X1 * df.X1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>36</td>\n",
       "      <td>216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>49</td>\n",
       "      <td>343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>64</td>\n",
       "      <td>512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>81</td>\n",
       "      <td>729</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   X1  X2   X3\n",
       "0   0   0    0\n",
       "1   1   1    1\n",
       "2   2   4    8\n",
       "3   3   9   27\n",
       "4   4  16   64\n",
       "5   5  25  125\n",
       "6   6  36  216\n",
       "7   7  49  343\n",
       "8   8  64  512\n",
       "9   9  81  729"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d1b08c5a20>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d1b18e3fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(linewidth=5,colormap='Set3', title='degree info')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal length (cm)',\n",
       " 'sepal width (cm)',\n",
       " 'petal length (cm)',\n",
       " 'petal width (cm)']"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'IRIS')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d1ac212ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x=iris.data[:,0],y=iris.data[:,1],c=iris.target, s=iris.data[:,2]*10)\n",
    "plt.xlabel('sepel length')\n",
    "plt.ylabel('sepel width')\n",
    "plt.title('IRIS')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d1b073ad68>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d1ac221da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = iris.data[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = iris.data[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1d1c09936d8>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d1c09210b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X,Y,s=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "income_data = pd.read_csv('https://raw.githubusercontent.com/zekelabs/machine-learning-for-beginners/master/data/adult.data.txt', header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "income_data.columns = ['age','workclass','fnlwgt','education','education-num','marital-status','occupation','relationship'\n",
    "        ,'race','sex','capital-gain','capital-loss','hours-per-week','native-country','Salary']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1d1c0fcdc50>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d1c0f50400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(income_data['education-num'],income_data['hours-per-week'], s=1,alpha=.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>6.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>7.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>7.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "0                  5.1               3.5                1.4               0.2\n",
       "1                  4.9               3.0                1.4               0.2\n",
       "2                  4.7               3.2                1.3               0.2\n",
       "3                  4.6               3.1                1.5               0.2\n",
       "4                  5.0               3.6                1.4               0.2\n",
       "5                  5.4               3.9                1.7               0.4\n",
       "6                  4.6               3.4                1.4               0.3\n",
       "7                  5.0               3.4                1.5               0.2\n",
       "8                  4.4               2.9                1.4               0.2\n",
       "9                  4.9               3.1                1.5               0.1\n",
       "10                 5.4               3.7                1.5               0.2\n",
       "11                 4.8               3.4                1.6               0.2\n",
       "12                 4.8               3.0                1.4               0.1\n",
       "13                 4.3               3.0                1.1               0.1\n",
       "14                 5.8               4.0                1.2               0.2\n",
       "15                 5.7               4.4                1.5               0.4\n",
       "16                 5.4               3.9                1.3               0.4\n",
       "17                 5.1               3.5                1.4               0.3\n",
       "18                 5.7               3.8                1.7               0.3\n",
       "19                 5.1               3.8                1.5               0.3\n",
       "20                 5.4               3.4                1.7               0.2\n",
       "21                 5.1               3.7                1.5               0.4\n",
       "22                 4.6               3.6                1.0               0.2\n",
       "23                 5.1               3.3                1.7               0.5\n",
       "24                 4.8               3.4                1.9               0.2\n",
       "25                 5.0               3.0                1.6               0.2\n",
       "26                 5.0               3.4                1.6               0.4\n",
       "27                 5.2               3.5                1.5               0.2\n",
       "28                 5.2               3.4                1.4               0.2\n",
       "29                 4.7               3.2                1.6               0.2\n",
       "..                 ...               ...                ...               ...\n",
       "120                6.9               3.2                5.7               2.3\n",
       "121                5.6               2.8                4.9               2.0\n",
       "122                7.7               2.8                6.7               2.0\n",
       "123                6.3               2.7                4.9               1.8\n",
       "124                6.7               3.3                5.7               2.1\n",
       "125                7.2               3.2                6.0               1.8\n",
       "126                6.2               2.8                4.8               1.8\n",
       "127                6.1               3.0                4.9               1.8\n",
       "128                6.4               2.8                5.6               2.1\n",
       "129                7.2               3.0                5.8               1.6\n",
       "130                7.4               2.8                6.1               1.9\n",
       "131                7.9               3.8                6.4               2.0\n",
       "132                6.4               2.8                5.6               2.2\n",
       "133                6.3               2.8                5.1               1.5\n",
       "134                6.1               2.6                5.6               1.4\n",
       "135                7.7               3.0                6.1               2.3\n",
       "136                6.3               3.4                5.6               2.4\n",
       "137                6.4               3.1                5.5               1.8\n",
       "138                6.0               3.0                4.8               1.8\n",
       "139                6.9               3.1                5.4               2.1\n",
       "140                6.7               3.1                5.6               2.4\n",
       "141                6.9               3.1                5.1               2.3\n",
       "142                5.8               2.7                5.1               1.9\n",
       "143                6.8               3.2                5.9               2.3\n",
       "144                6.7               3.3                5.7               2.5\n",
       "145                6.7               3.0                5.2               2.3\n",
       "146                6.3               2.5                5.0               1.9\n",
       "147                6.5               3.0                5.2               2.0\n",
       "148                6.2               3.4                5.4               2.3\n",
       "149                5.9               3.0                5.1               1.8\n",
       "\n",
       "[150 rows x 4 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(data = iris.data, columns=iris.feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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